import os import imageio import numpy as np import torch import rembg from PIL import Image from torchvision.transforms import v2 from pytorch_lightning import seed_everything from omegaconf import OmegaConf from einops import rearrange, repeat from tqdm import tqdm from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler from instantmesh.src.utils.train_util import instantiate_from_config from instantmesh.src.utils.camera_util import ( FOV_to_intrinsics, get_zero123plus_input_cameras, get_circular_camera_poses, ) from instantmesh.src.utils.mesh_util import save_obj, save_glb from instantmesh.src.utils.infer_util import remove_background, resize_foreground, images_to_video import tempfile from functools import partial from huggingface_hub import hf_hub_download import gradio as gr import shutil import spaces def get_render_cameras(batch_size=1, M=120, radius=2.5, elevation=10.0, is_flexicubes=False): """ Get the rendering camera parameters. """ c2ws = get_circular_camera_poses(M=M, radius=radius, elevation=elevation) if is_flexicubes: cameras = torch.linalg.inv(c2ws) cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1, 1) else: extrinsics = c2ws.flatten(-2) intrinsics = FOV_to_intrinsics(50.0).unsqueeze(0).repeat(M, 1, 1).float().flatten(-2) cameras = torch.cat([extrinsics, intrinsics], dim=-1) cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1) return cameras import shutil def find_cuda(): # Check if CUDA_HOME or CUDA_PATH environment variables are set cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH') if cuda_home and os.path.exists(cuda_home): return cuda_home # Search for the nvcc executable in the system's PATH nvcc_path = shutil.which('nvcc') if nvcc_path: # Remove the 'bin/nvcc' part to get the CUDA installation path cuda_path = os.path.dirname(os.path.dirname(nvcc_path)) return cuda_path return None def check_input_image(input_image): if input_image is None: raise gr.Error("No image uploaded!") def preprocess(input_image, do_remove_background): rembg_session = rembg.new_session() if do_remove_background else None if do_remove_background: input_image = remove_background(input_image, rembg_session) input_image = resize_foreground(input_image, 0.85) return input_image @spaces.GPU def generate_mvs(input_image, sample_steps, sample_seed): seed_everything(sample_seed) # sampling z123_image = pipeline( input_image, num_inference_steps=sample_steps ).images[0] show_image = np.asarray(z123_image, dtype=np.uint8) show_image = torch.from_numpy(show_image) # (960, 640, 3) show_image = rearrange(show_image, '(n h) (m w) c -> (n m) h w c', n=3, m=2) show_image = rearrange(show_image, '(n m) h w c -> (n h) (m w) c', n=2, m=3) show_image = Image.fromarray(show_image.numpy()) return z123_image, show_image @spaces.GPU def make3d(images): global model if IS_FLEXICUBES: model.init_flexicubes_geometry(device, use_renderer=False) model = model.eval() images = np.asarray(images, dtype=np.float32) / 255.0 images = torch.from_numpy(images).permute(2, 0, 1).contiguous().float() # (3, 960, 640) images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2) # (6, 3, 320, 320) input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=4.0).to(device) render_cameras = get_render_cameras(batch_size=1, radius=2.5, is_flexicubes=IS_FLEXICUBES).to(device) images = images.unsqueeze(0).to(device) images = v2.functional.resize(images, (320, 320), interpolation=3, antialias=True).clamp(0, 1) mesh_fpath = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False).name print(mesh_fpath) mesh_basename = os.path.basename(mesh_fpath).split('.')[0] mesh_dirname = os.path.dirname(mesh_fpath) video_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.mp4") mesh_glb_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.glb") with torch.no_grad(): # get triplane planes = model.forward_planes(images, input_cameras) # # get video # chunk_size = 20 if IS_FLEXICUBES else 1 # render_size = 384 # frames = [] # for i in tqdm(range(0, render_cameras.shape[1], chunk_size)): # if IS_FLEXICUBES: # frame = model.forward_geometry( # planes, # render_cameras[:, i:i+chunk_size], # render_size=render_size, # )['img'] # else: # frame = model.synthesizer( # planes, # cameras=render_cameras[:, i:i+chunk_size], # render_size=render_size, # )['images_rgb'] # frames.append(frame) # frames = torch.cat(frames, dim=1) # images_to_video( # frames[0], # video_fpath, # fps=30, # ) # print(f"Video saved to {video_fpath}") # get mesh mesh_out = model.extract_mesh( planes, use_texture_map=False, **infer_config, ) vertices, faces, vertex_colors = mesh_out vertices = vertices[:, [1, 2, 0]] save_glb(vertices, faces, vertex_colors, mesh_glb_fpath) save_obj(vertices, faces, vertex_colors, mesh_fpath) print(f"Mesh saved to {mesh_fpath}") return mesh_fpath, mesh_glb_fpath